Overview

Dataset statistics

Number of variables25
Number of observations4745
Missing cells0
Missing cells (%)0.0%
Duplicate rows42
Duplicate rows (%)0.9%
Total size in memory1.1 MiB
Average record size in memory235.8 B

Variable types

Text7
Numeric16
Categorical2

Alerts

Dataset has 42 (0.9%) duplicate rowsDuplicates
content_rating is highly imbalanced (50.8%)Imbalance
budget is highly skewed (γ1 = 49.20095838)Skewed
director_fb_likes has 835 (17.6%) zerosZeros
actor_3_fb_likes has 66 (1.4%) zerosZeros
facenumber_in_poster has 2035 (42.9%) zerosZeros
movie_fb_likes has 2104 (44.3%) zerosZeros

Reproduction

Analysis started2024-04-10 12:32:52.580708
Analysis finished2024-04-10 12:34:50.520855
Duration1 minute and 57.94 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

Distinct2244
Distinct (%)47.3%
Missing0
Missing (%)0.0%
Memory size203.2 KiB
2024-04-10T14:34:53.437040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length32
Median length24
Mean length13.079663
Min length3

Characters and Unicode

Total characters62063
Distinct characters76
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1371 ?
Unique (%)28.9%

Sample

1st rowJames Cameron
2nd rowGore Verbinski
3rd rowSam Mendes
4th rowChristopher Nolan
5th rowAndrew Stanton
ValueCountFrequency (%)
john 175
 
1.8%
david 145
 
1.5%
michael 124
 
1.3%
james 85
 
0.9%
peter 84
 
0.9%
robert 81
 
0.8%
richard 79
 
0.8%
paul 78
 
0.8%
scott 65
 
0.7%
lee 57
 
0.6%
Other values (2797) 8889
90.1%
2024-04-10T14:34:54.863425image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 5874
 
9.5%
5117
 
8.2%
a 5022
 
8.1%
n 4474
 
7.2%
r 4280
 
6.9%
o 3657
 
5.9%
i 3532
 
5.7%
l 2854
 
4.6%
t 2236
 
3.6%
s 2001
 
3.2%
Other values (66) 23016
37.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 62063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 5874
 
9.5%
5117
 
8.2%
a 5022
 
8.1%
n 4474
 
7.2%
r 4280
 
6.9%
o 3657
 
5.9%
i 3532
 
5.7%
l 2854
 
4.6%
t 2236
 
3.6%
s 2001
 
3.2%
Other values (66) 23016
37.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 62063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 5874
 
9.5%
5117
 
8.2%
a 5022
 
8.1%
n 4474
 
7.2%
r 4280
 
6.9%
o 3657
 
5.9%
i 3532
 
5.7%
l 2854
 
4.6%
t 2236
 
3.6%
s 2001
 
3.2%
Other values (66) 23016
37.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 62063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 5874
 
9.5%
5117
 
8.2%
a 5022
 
8.1%
n 4474
 
7.2%
r 4280
 
6.9%
o 3657
 
5.9%
i 3532
 
5.7%
l 2854
 
4.6%
t 2236
 
3.6%
s 2001
 
3.2%
Other values (66) 23016
37.1%

num_critic_for_reviews
Real number (ℝ)

Distinct527
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean146.08662
Minimum1
Maximum813
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.2 KiB
2024-04-10T14:34:55.815011image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q157
median117
Q3201
95-th percentile391
Maximum813
Range812
Interquartile range (IQR)144

Descriptive statistics

Standard deviation121.11349
Coefficient of variation (CV)0.8290526
Kurtosis2.8862095
Mean146.08662
Median Absolute Deviation (MAD)68
Skewness1.5052961
Sum693181
Variance14668.478
MonotonicityNot monotonic
2024-04-10T14:34:56.343603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81 33
 
0.7%
43 30
 
0.6%
63 29
 
0.6%
25 29
 
0.6%
46 28
 
0.6%
16 28
 
0.6%
97 28
 
0.6%
50 28
 
0.6%
112 28
 
0.6%
29 28
 
0.6%
Other values (517) 4456
93.9%
ValueCountFrequency (%)
1 22
0.5%
2 17
0.4%
3 9
 
0.2%
4 14
0.3%
5 26
0.5%
6 15
0.3%
7 18
0.4%
8 25
0.5%
9 24
0.5%
10 26
0.5%
ValueCountFrequency (%)
813 1
< 0.1%
775 1
< 0.1%
765 1
< 0.1%
750 2
< 0.1%
739 1
< 0.1%
738 1
< 0.1%
733 1
< 0.1%
723 1
< 0.1%
712 1
< 0.1%
703 2
< 0.1%

duration
Real number (ℝ)

Distinct164
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108.62044
Minimum14
Maximum330
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.2 KiB
2024-04-10T14:34:56.758360image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile84
Q194
median104
Q3118
95-th percentile146
Maximum330
Range316
Interquartile range (IQR)24

Descriptive statistics

Standard deviation22.523631
Coefficient of variation (CV)0.20736089
Kurtosis11.772115
Mean108.62044
Median Absolute Deviation (MAD)11
Skewness2.226497
Sum515404
Variance507.31396
MonotonicityNot monotonic
2024-04-10T14:34:57.102082image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 143
 
3.0%
100 136
 
2.9%
101 133
 
2.8%
98 130
 
2.7%
97 125
 
2.6%
93 122
 
2.6%
95 121
 
2.6%
99 120
 
2.5%
94 120
 
2.5%
106 109
 
2.3%
Other values (154) 3486
73.5%
ValueCountFrequency (%)
14 1
< 0.1%
20 1
< 0.1%
25 1
< 0.1%
34 1
< 0.1%
37 1
< 0.1%
41 1
< 0.1%
45 2
< 0.1%
46 1
< 0.1%
47 1
< 0.1%
53 1
< 0.1%
ValueCountFrequency (%)
330 1
< 0.1%
325 1
< 0.1%
300 1
< 0.1%
293 1
< 0.1%
289 1
< 0.1%
280 1
< 0.1%
271 1
< 0.1%
270 1
< 0.1%
251 2
< 0.1%
240 2
< 0.1%

director_fb_likes
Real number (ℝ)

ZEROS 

Distinct429
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean707.98778
Minimum0
Maximum23000
Zeros835
Zeros (%)17.6%
Negative0
Negative (%)0.0%
Memory size203.2 KiB
2024-04-10T14:34:58.026045image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median52
Q3209
95-th percentile1000
Maximum23000
Range23000
Interquartile range (IQR)201

Descriptive statistics

Standard deviation2853.5003
Coefficient of variation (CV)4.0304373
Kurtosis26.160629
Mean707.98778
Median Absolute Deviation (MAD)52
Skewness5.1294727
Sum3359402
Variance8142464.1
MonotonicityNot monotonic
2024-04-10T14:34:58.595046image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 835
 
17.6%
3 65
 
1.4%
6 61
 
1.3%
7 58
 
1.2%
11 57
 
1.2%
2 56
 
1.2%
4 54
 
1.1%
10 51
 
1.1%
12 48
 
1.0%
5 48
 
1.0%
Other values (419) 3412
71.9%
ValueCountFrequency (%)
0 835
17.6%
2 56
 
1.2%
3 65
 
1.4%
4 54
 
1.1%
5 48
 
1.0%
6 61
 
1.3%
7 58
 
1.2%
8 47
 
1.0%
9 46
 
1.0%
10 51
 
1.1%
ValueCountFrequency (%)
23000 1
 
< 0.1%
22000 8
 
0.2%
21000 10
 
0.2%
18000 4
 
0.1%
17000 20
0.4%
16000 28
0.6%
15000 2
 
< 0.1%
14000 30
0.6%
13000 26
0.5%
12000 17
0.4%

actor_3_fb_likes
Real number (ℝ)

ZEROS 

Distinct905
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean667.40506
Minimum0
Maximum23000
Zeros66
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size203.2 KiB
2024-04-10T14:34:59.027471image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q1141
median384
Q3642
95-th percentile1000
Maximum23000
Range23000
Interquartile range (IQR)501

Descriptive statistics

Standard deviation1708.8646
Coefficient of variation (CV)2.5604609
Kurtosis57.241954
Mean667.40506
Median Absolute Deviation (MAD)251
Skewness7.0857549
Sum3166837
Variance2920218.2
MonotonicityNot monotonic
2024-04-10T14:34:59.428080image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 124
 
2.6%
0 66
 
1.4%
11000 29
 
0.6%
2000 27
 
0.6%
3000 26
 
0.5%
3 23
 
0.5%
826 22
 
0.5%
7 21
 
0.4%
249 19
 
0.4%
4 18
 
0.4%
Other values (895) 4370
92.1%
ValueCountFrequency (%)
0 66
1.4%
2 16
 
0.3%
3 23
 
0.5%
4 18
 
0.4%
5 12
 
0.3%
6 17
 
0.4%
7 21
 
0.4%
8 15
 
0.3%
9 14
 
0.3%
10 12
 
0.3%
ValueCountFrequency (%)
23000 2
 
< 0.1%
20000 1
 
< 0.1%
19000 5
 
0.1%
17000 1
 
< 0.1%
16000 3
 
0.1%
15000 1
 
< 0.1%
14000 6
 
0.1%
13000 5
 
0.1%
12000 8
 
0.2%
11000 29
0.6%
Distinct2824
Distinct (%)59.5%
Missing0
Missing (%)0.0%
Memory size203.2 KiB
2024-04-10T14:35:00.556580image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length28
Median length25
Mean length13.069547
Min length3

Characters and Unicode

Total characters62015
Distinct characters78
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1915 ?
Unique (%)40.4%

Sample

1st rowJoel David Moore
2nd rowOrlando Bloom
3rd rowRory Kinnear
4th rowChristian Bale
5th rowSamantha Morton
ValueCountFrequency (%)
michael 94
 
1.0%
david 54
 
0.6%
john 53
 
0.5%
james 50
 
0.5%
tom 48
 
0.5%
scott 48
 
0.5%
jason 43
 
0.4%
robert 42
 
0.4%
kevin 39
 
0.4%
bruce 36
 
0.4%
Other values (3619) 9306
94.8%
2024-04-10T14:35:02.251722image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 5867
 
9.5%
a 5568
 
9.0%
5068
 
8.2%
n 4486
 
7.2%
r 4154
 
6.7%
i 3805
 
6.1%
o 3427
 
5.5%
l 3232
 
5.2%
t 2210
 
3.6%
s 2039
 
3.3%
Other values (68) 22159
35.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 62015
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 5867
 
9.5%
a 5568
 
9.0%
5068
 
8.2%
n 4486
 
7.2%
r 4154
 
6.7%
i 3805
 
6.1%
o 3427
 
5.5%
l 3232
 
5.2%
t 2210
 
3.6%
s 2039
 
3.3%
Other values (68) 22159
35.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 62015
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 5867
 
9.5%
a 5568
 
9.0%
5068
 
8.2%
n 4486
 
7.2%
r 4154
 
6.7%
i 3805
 
6.1%
o 3427
 
5.5%
l 3232
 
5.2%
t 2210
 
3.6%
s 2039
 
3.3%
Other values (68) 22159
35.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 62015
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 5867
 
9.5%
a 5568
 
9.0%
5068
 
8.2%
n 4486
 
7.2%
r 4154
 
6.7%
i 3805
 
6.1%
o 3427
 
5.5%
l 3232
 
5.2%
t 2210
 
3.6%
s 2039
 
3.3%
Other values (68) 22159
35.7%

actor_1_fb_likes
Real number (ℝ)

Distinct843
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6821.6906
Minimum0
Maximum640000
Zeros14
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size203.2 KiB
2024-04-10T14:35:02.673755image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile117
Q1638
median1000
Q311000
95-th percentile24000
Maximum640000
Range640000
Interquartile range (IQR)10362

Descriptive statistics

Standard deviation14943.707
Coefficient of variation (CV)2.1906164
Kurtosis722.02214
Mean6821.6906
Median Absolute Deviation (MAD)792
Skewness19.530386
Sum32368922
Variance2.2331439 × 108
MonotonicityNot monotonic
2024-04-10T14:35:03.211771image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 421
 
8.9%
11000 209
 
4.4%
2000 190
 
4.0%
3000 151
 
3.2%
12000 135
 
2.8%
13000 126
 
2.7%
14000 122
 
2.6%
10000 109
 
2.3%
18000 109
 
2.3%
22000 80
 
1.7%
Other values (833) 3093
65.2%
ValueCountFrequency (%)
0 14
0.3%
2 6
0.1%
3 2
 
< 0.1%
4 2
 
< 0.1%
5 4
 
0.1%
6 3
 
0.1%
7 2
 
< 0.1%
9 3
 
0.1%
11 2
 
< 0.1%
12 3
 
0.1%
ValueCountFrequency (%)
640000 1
 
< 0.1%
260000 2
 
< 0.1%
164000 2
 
< 0.1%
137000 2
 
< 0.1%
87000 8
 
0.2%
77000 1
 
< 0.1%
49000 27
0.6%
46000 1
 
< 0.1%
45000 5
 
0.1%
44000 2
 
< 0.1%

gross
Real number (ℝ)

Distinct4146
Distinct (%)87.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45202964
Minimum162
Maximum7.6050585 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.2 KiB
2024-04-10T14:35:03.623752image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum162
5-th percentile100751
Q16531491
median24848292
Q354557348
95-th percentile1.7096688 × 108
Maximum7.6050585 × 108
Range7.6050568 × 108
Interquartile range (IQR)48025857

Descriptive statistics

Standard deviation64598174
Coefficient of variation (CV)1.4290694
Kurtosis17.370468
Mean45202964
Median Absolute Deviation (MAD)20807704
Skewness3.3896832
Sum2.1448806 × 1011
Variance4.1729241 × 1015
MonotonicityNot monotonic
2024-04-10T14:35:04.446235image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24848292 461
 
9.7%
5000000 4
 
0.1%
34964818 3
 
0.1%
3000000 3
 
0.1%
144512310 3
 
0.1%
5773519 3
 
0.1%
218051260 3
 
0.1%
177343675 3
 
0.1%
7000000 3
 
0.1%
47000000 3
 
0.1%
Other values (4136) 4256
89.7%
ValueCountFrequency (%)
162 1
< 0.1%
423 1
< 0.1%
607 1
< 0.1%
703 1
< 0.1%
721 1
< 0.1%
728 1
< 0.1%
828 1
< 0.1%
1029 1
< 0.1%
1100 1
< 0.1%
1111 1
< 0.1%
ValueCountFrequency (%)
760505847 1
< 0.1%
658672302 1
< 0.1%
652177271 1
< 0.1%
623279547 2
< 0.1%
533316061 1
< 0.1%
474544677 1
< 0.1%
460935665 1
< 0.1%
458991599 1
< 0.1%
448130642 1
< 0.1%
436471036 1
< 0.1%

genres
Text

Distinct878
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Memory size203.2 KiB
2024-04-10T14:35:05.014987image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length64
Median length53
Mean length20.553214
Min length5

Characters and Unicode

Total characters97525
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique478 ?
Unique (%)10.1%

Sample

1st rowAction|Adventure|Fantasy|Sci-Fi
2nd rowAction|Adventure|Fantasy
3rd rowAction|Adventure|Thriller
4th rowAction|Thriller
5th rowAction|Adventure|Sci-Fi
ValueCountFrequency (%)
drama 210
 
4.4%
comedy 190
 
4.0%
comedy|drama 182
 
3.8%
comedy|drama|romance 182
 
3.8%
comedy|romance 150
 
3.2%
drama|romance 148
 
3.1%
crime|drama|thriller 95
 
2.0%
horror 65
 
1.4%
action|crime|thriller 64
 
1.3%
action|crime|drama|thriller 64
 
1.3%
Other values (868) 3395
71.5%
2024-04-10T14:35:06.170487image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 9993
 
10.2%
| 9056
 
9.3%
a 8556
 
8.8%
e 7578
 
7.8%
m 6981
 
7.2%
i 6285
 
6.4%
o 6041
 
6.2%
y 4393
 
4.5%
n 4300
 
4.4%
t 3843
 
3.9%
Other values (23) 30499
31.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 97525
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 9993
 
10.2%
| 9056
 
9.3%
a 8556
 
8.8%
e 7578
 
7.8%
m 6981
 
7.2%
i 6285
 
6.4%
o 6041
 
6.2%
y 4393
 
4.5%
n 4300
 
4.4%
t 3843
 
3.9%
Other values (23) 30499
31.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 97525
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 9993
 
10.2%
| 9056
 
9.3%
a 8556
 
8.8%
e 7578
 
7.8%
m 6981
 
7.2%
i 6285
 
6.4%
o 6041
 
6.2%
y 4393
 
4.5%
n 4300
 
4.4%
t 3843
 
3.9%
Other values (23) 30499
31.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 97525
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 9993
 
10.2%
| 9056
 
9.3%
a 8556
 
8.8%
e 7578
 
7.8%
m 6981
 
7.2%
i 6285
 
6.4%
o 6041
 
6.2%
y 4393
 
4.5%
n 4300
 
4.4%
t 3843
 
3.9%
Other values (23) 30499
31.3%
Distinct1928
Distinct (%)40.6%
Missing0
Missing (%)0.0%
Memory size203.2 KiB
2024-04-10T14:35:06.939044image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length27
Median length24
Mean length13.179979
Min length4

Characters and Unicode

Total characters62539
Distinct characters75
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1227 ?
Unique (%)25.9%

Sample

1st rowCCH Pounder
2nd rowJohnny Depp
3rd rowChristoph Waltz
4th rowTom Hardy
5th rowDaryl Sabara
ValueCountFrequency (%)
robert 107
 
1.1%
tom 91
 
0.9%
michael 83
 
0.8%
de 56
 
0.6%
jason 54
 
0.5%
james 50
 
0.5%
steve 50
 
0.5%
bruce 49
 
0.5%
jr 49
 
0.5%
niro 48
 
0.5%
Other values (2681) 9197
93.5%
2024-04-10T14:35:08.277831image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 5878
 
9.4%
a 5359
 
8.6%
5089
 
8.1%
n 4542
 
7.3%
r 4040
 
6.5%
i 3995
 
6.4%
o 3675
 
5.9%
l 3109
 
5.0%
t 2449
 
3.9%
s 2216
 
3.5%
Other values (65) 22187
35.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 62539
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 5878
 
9.4%
a 5359
 
8.6%
5089
 
8.1%
n 4542
 
7.3%
r 4040
 
6.5%
i 3995
 
6.4%
o 3675
 
5.9%
l 3109
 
5.0%
t 2449
 
3.9%
s 2216
 
3.5%
Other values (65) 22187
35.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 62539
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 5878
 
9.4%
a 5359
 
8.6%
5089
 
8.1%
n 4542
 
7.3%
r 4040
 
6.5%
i 3995
 
6.4%
o 3675
 
5.9%
l 3109
 
5.0%
t 2449
 
3.9%
s 2216
 
3.5%
Other values (65) 22187
35.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 62539
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 5878
 
9.4%
a 5359
 
8.6%
5089
 
8.1%
n 4542
 
7.3%
r 4040
 
6.5%
i 3995
 
6.4%
o 3675
 
5.9%
l 3109
 
5.0%
t 2449
 
3.9%
s 2216
 
3.5%
Other values (65) 22187
35.5%
Distinct4624
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Memory size203.2 KiB
2024-04-10T14:35:09.063830image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length87
Median length59
Mean length16.291254
Min length2

Characters and Unicode

Total characters77302
Distinct characters92
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4509 ?
Unique (%)95.0%

Sample

1st rowAvatar 
2nd rowPirates of the Caribbean: At World's End 
3rd rowSpectre 
4th rowThe Dark Knight Rises 
5th rowJohn Carter 
ValueCountFrequency (%)
the 1513
 
11.5%
of 452
 
3.4%
a 178
 
1.4%
and 138
 
1.1%
in 115
 
0.9%
2 104
 
0.8%
to 99
 
0.8%
76
 
0.6%
man 64
 
0.5%
love 53
 
0.4%
Other values (4679) 10342
78.7%
2024-04-10T14:35:10.211135image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8389
 
10.9%
e 7420
 
9.6%
  4745
 
6.1%
a 4561
 
5.9%
o 4390
 
5.7%
r 3892
 
5.0%
n 3881
 
5.0%
i 3714
 
4.8%
t 3590
 
4.6%
s 2841
 
3.7%
Other values (82) 29879
38.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 77302
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8389
 
10.9%
e 7420
 
9.6%
  4745
 
6.1%
a 4561
 
5.9%
o 4390
 
5.7%
r 3892
 
5.0%
n 3881
 
5.0%
i 3714
 
4.8%
t 3590
 
4.6%
s 2841
 
3.7%
Other values (82) 29879
38.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 77302
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8389
 
10.9%
e 7420
 
9.6%
  4745
 
6.1%
a 4561
 
5.9%
o 4390
 
5.7%
r 3892
 
5.0%
n 3881
 
5.0%
i 3714
 
4.8%
t 3590
 
4.6%
s 2841
 
3.7%
Other values (82) 29879
38.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 77302
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8389
 
10.9%
e 7420
 
9.6%
  4745
 
6.1%
a 4561
 
5.9%
o 4390
 
5.7%
r 3892
 
5.0%
n 3881
 
5.0%
i 3714
 
4.8%
t 3590
 
4.6%
s 2841
 
3.7%
Other values (82) 29879
38.7%

num_voted_users
Real number (ℝ)

Distinct4593
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88006.199
Minimum5
Maximum1689764
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.2 KiB
2024-04-10T14:35:10.684100image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile1106
Q110832
median38142
Q3102123
95-th percentile343573.2
Maximum1689764
Range1689759
Interquartile range (IQR)91291

Descriptive statistics

Standard deviation141146.8
Coefficient of variation (CV)1.6038279
Kurtosis23.484704
Mean88006.199
Median Absolute Deviation (MAD)32988
Skewness3.9503719
Sum4.1758942 × 108
Variance1.9922418 × 1010
MonotonicityNot monotonic
2024-04-10T14:35:11.110749image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
374 3
 
0.1%
3119 3
 
0.1%
3665 3
 
0.1%
6025 3
 
0.1%
2541 3
 
0.1%
3943 2
 
< 0.1%
53 2
 
< 0.1%
806 2
 
< 0.1%
25870 2
 
< 0.1%
3662 2
 
< 0.1%
Other values (4583) 4720
99.5%
ValueCountFrequency (%)
5 2
< 0.1%
19 1
< 0.1%
28 1
< 0.1%
37 1
< 0.1%
40 1
< 0.1%
47 1
< 0.1%
48 1
< 0.1%
50 1
< 0.1%
53 2
< 0.1%
59 1
< 0.1%
ValueCountFrequency (%)
1689764 1
< 0.1%
1676169 1
< 0.1%
1468200 1
< 0.1%
1347461 1
< 0.1%
1324680 1
< 0.1%
1251222 1
< 0.1%
1238746 1
< 0.1%
1217752 1
< 0.1%
1215718 1
< 0.1%
1155770 1
< 0.1%

cast_total_fb_likes
Real number (ℝ)

Distinct3841
Distinct (%)80.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10097.163
Minimum0
Maximum656730
Zeros14
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size203.2 KiB
2024-04-10T14:35:11.506794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile243
Q11509
median3233
Q314486
95-th percentile37757.4
Maximum656730
Range656730
Interquartile range (IQR)12977

Descriptive statistics

Standard deviation18236.402
Coefficient of variation (CV)1.8060916
Kurtosis369.19477
Mean10097.163
Median Absolute Deviation (MAD)2424
Skewness12.857413
Sum47911040
Variance3.3256636 × 108
MonotonicityNot monotonic
2024-04-10T14:35:11.903916image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14
 
0.3%
2020 6
 
0.1%
1044 5
 
0.1%
673 5
 
0.1%
29 5
 
0.1%
1554 4
 
0.1%
2486 4
 
0.1%
2321 4
 
0.1%
1136 4
 
0.1%
2348 4
 
0.1%
Other values (3831) 4690
98.8%
ValueCountFrequency (%)
0 14
0.3%
2 4
 
0.1%
4 2
 
< 0.1%
5 3
 
0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
8 2
 
< 0.1%
11 1
 
< 0.1%
13 1
 
< 0.1%
15 2
 
< 0.1%
ValueCountFrequency (%)
656730 1
< 0.1%
303717 1
< 0.1%
283939 1
< 0.1%
263584 1
< 0.1%
170118 1
< 0.1%
140268 1
< 0.1%
137712 1
< 0.1%
120797 1
< 0.1%
108016 1
< 0.1%
106759 1
< 0.1%
Distinct3312
Distinct (%)69.8%
Missing0
Missing (%)0.0%
Memory size203.2 KiB
2024-04-10T14:35:12.603546image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length29
Median length25
Mean length13.073551
Min length3

Characters and Unicode

Total characters62034
Distinct characters81
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2474 ?
Unique (%)52.1%

Sample

1st rowWes Studi
2nd rowJack Davenport
3rd rowStephanie Sigman
4th rowJoseph Gordon-Levitt
5th rowPolly Walker
ValueCountFrequency (%)
michael 79
 
0.8%
john 73
 
0.7%
david 68
 
0.7%
james 64
 
0.7%
robert 46
 
0.5%
kevin 39
 
0.4%
paul 39
 
0.4%
tom 38
 
0.4%
peter 37
 
0.4%
steve 36
 
0.4%
Other values (4097) 9311
94.7%
2024-04-10T14:35:14.001237image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 5867
 
9.5%
a 5643
 
9.1%
5085
 
8.2%
n 4338
 
7.0%
r 3959
 
6.4%
i 3752
 
6.0%
o 3370
 
5.4%
l 3321
 
5.4%
t 2224
 
3.6%
s 2209
 
3.6%
Other values (71) 22266
35.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 62034
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 5867
 
9.5%
a 5643
 
9.1%
5085
 
8.2%
n 4338
 
7.0%
r 3959
 
6.4%
i 3752
 
6.0%
o 3370
 
5.4%
l 3321
 
5.4%
t 2224
 
3.6%
s 2209
 
3.6%
Other values (71) 22266
35.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 62034
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 5867
 
9.5%
a 5643
 
9.1%
5085
 
8.2%
n 4338
 
7.0%
r 3959
 
6.4%
i 3752
 
6.0%
o 3370
 
5.4%
l 3321
 
5.4%
t 2224
 
3.6%
s 2209
 
3.6%
Other values (71) 22266
35.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 62034
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 5867
 
9.5%
a 5643
 
9.1%
5085
 
8.2%
n 4338
 
7.0%
r 3959
 
6.4%
i 3752
 
6.0%
o 3370
 
5.4%
l 3321
 
5.4%
t 2224
 
3.6%
s 2209
 
3.6%
Other values (71) 22266
35.9%

facenumber_in_poster
Real number (ℝ)

ZEROS 

Distinct19
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3595364
Minimum0
Maximum43
Zeros2035
Zeros (%)42.9%
Negative0
Negative (%)0.0%
Memory size203.2 KiB
2024-04-10T14:35:14.351274image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum43
Range43
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.0082372
Coefficient of variation (CV)1.4771486
Kurtosis55.329778
Mean1.3595364
Median Absolute Deviation (MAD)1
Skewness4.5395464
Sum6451
Variance4.0330166
MonotonicityNot monotonic
2024-04-10T14:35:14.635230image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 2035
42.9%
1 1186
25.0%
2 673
 
14.2%
3 364
 
7.7%
4 193
 
4.1%
5 101
 
2.1%
6 68
 
1.4%
7 45
 
0.9%
8 34
 
0.7%
9 16
 
0.3%
Other values (9) 30
 
0.6%
ValueCountFrequency (%)
0 2035
42.9%
1 1186
25.0%
2 673
 
14.2%
3 364
 
7.7%
4 193
 
4.1%
5 101
 
2.1%
6 68
 
1.4%
7 45
 
0.9%
8 34
 
0.7%
9 16
 
0.3%
ValueCountFrequency (%)
43 1
 
< 0.1%
31 1
 
< 0.1%
19 1
 
< 0.1%
15 5
 
0.1%
14 1
 
< 0.1%
13 2
 
< 0.1%
12 4
 
0.1%
11 5
 
0.1%
10 10
0.2%
9 16
0.3%
Distinct4620
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Memory size203.2 KiB
2024-04-10T14:35:15.368785image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length149
Median length102
Mean length52.409905
Min length2

Characters and Unicode

Total characters248685
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4503 ?
Unique (%)94.9%

Sample

1st rowavatar|future|marine|native|paraplegic
2nd rowgoddess|marriage ceremony|marriage proposal|pirate|singapore
3rd rowbomb|espionage|sequel|spy|terrorist
4th rowdeception|imprisonment|lawlessness|police officer|terrorist plot
5th rowalien|american civil war|male nipple|mars|princess
ValueCountFrequency (%)
in 314
 
1.8%
of 215
 
1.2%
on 196
 
1.1%
the 187
 
1.1%
a 182
 
1.0%
to 178
 
1.0%
york 120
 
0.7%
female 102
 
0.6%
by 99
 
0.6%
based 98
 
0.6%
Other values (11164) 15734
90.3%
2024-04-10T14:35:17.637936image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 24053
 
9.7%
a 18996
 
7.6%
| 18684
 
7.5%
i 18136
 
7.3%
r 17581
 
7.1%
t 15663
 
6.3%
n 15225
 
6.1%
o 15001
 
6.0%
s 12894
 
5.2%
12680
 
5.1%
Other values (32) 79772
32.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 248685
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 24053
 
9.7%
a 18996
 
7.6%
| 18684
 
7.5%
i 18136
 
7.3%
r 17581
 
7.1%
t 15663
 
6.3%
n 15225
 
6.1%
o 15001
 
6.0%
s 12894
 
5.2%
12680
 
5.1%
Other values (32) 79772
32.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 248685
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 24053
 
9.7%
a 18996
 
7.6%
| 18684
 
7.5%
i 18136
 
7.3%
r 17581
 
7.1%
t 15663
 
6.3%
n 15225
 
6.1%
o 15001
 
6.0%
s 12894
 
5.2%
12680
 
5.1%
Other values (32) 79772
32.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 248685
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 24053
 
9.7%
a 18996
 
7.6%
| 18684
 
7.5%
i 18136
 
7.3%
r 17581
 
7.1%
t 15663
 
6.3%
n 15225
 
6.1%
o 15001
 
6.0%
s 12894
 
5.2%
12680
 
5.1%
Other values (32) 79772
32.1%

num_user_for_reviews
Real number (ℝ)

Distinct953
Distinct (%)20.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean285.58714
Minimum1
Maximum5060
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.2 KiB
2024-04-10T14:35:18.006954image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15
Q175
median167
Q3341
95-th percentile928
Maximum5060
Range5059
Interquartile range (IQR)266

Descriptive statistics

Standard deviation383.9471
Coefficient of variation (CV)1.3444131
Kurtosis25.692807
Mean285.58714
Median Absolute Deviation (MAD)114
Skewness4.0715798
Sum1355111
Variance147415.37
MonotonicityNot monotonic
2024-04-10T14:35:18.397202image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26 30
 
0.6%
50 25
 
0.5%
32 24
 
0.5%
14 22
 
0.5%
53 22
 
0.5%
31 22
 
0.5%
21 22
 
0.5%
27 21
 
0.4%
10 21
 
0.4%
39 21
 
0.4%
Other values (943) 4515
95.2%
ValueCountFrequency (%)
1 18
0.4%
2 10
0.2%
3 17
0.4%
4 11
0.2%
5 14
0.3%
6 17
0.4%
7 12
0.3%
8 14
0.3%
9 17
0.4%
10 21
0.4%
ValueCountFrequency (%)
5060 1
< 0.1%
4667 1
< 0.1%
4144 1
< 0.1%
3646 1
< 0.1%
3597 1
< 0.1%
3516 1
< 0.1%
3400 1
< 0.1%
3286 1
< 0.1%
3189 1
< 0.1%
3054 1
< 0.1%

country
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size203.2 KiB
USA
3607 
Other
713 
UK
425 

Length

Max length5
Median length3
Mean length3.2109589
Min length2

Characters and Unicode

Total characters15236
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSA
2nd rowUSA
3rd rowUK
4th rowUSA
5th rowUSA

Common Values

ValueCountFrequency (%)
USA 3607
76.0%
Other 713
 
15.0%
UK 425
 
9.0%

Length

2024-04-10T14:35:18.747236image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-10T14:35:19.047235image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
usa 3607
76.0%
other 713
 
15.0%
uk 425
 
9.0%

Most occurring characters

ValueCountFrequency (%)
U 4032
26.5%
S 3607
23.7%
A 3607
23.7%
O 713
 
4.7%
t 713
 
4.7%
h 713
 
4.7%
e 713
 
4.7%
r 713
 
4.7%
K 425
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15236
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 4032
26.5%
S 3607
23.7%
A 3607
23.7%
O 713
 
4.7%
t 713
 
4.7%
h 713
 
4.7%
e 713
 
4.7%
r 713
 
4.7%
K 425
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15236
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 4032
26.5%
S 3607
23.7%
A 3607
23.7%
O 713
 
4.7%
t 713
 
4.7%
h 713
 
4.7%
e 713
 
4.7%
r 713
 
4.7%
K 425
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15236
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 4032
26.5%
S 3607
23.7%
A 3607
23.7%
O 713
 
4.7%
t 713
 
4.7%
h 713
 
4.7%
e 713
 
4.7%
r 713
 
4.7%
K 425
 
2.8%

content_rating
Categorical

IMBALANCE 

Distinct15
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size203.2 KiB
R
2255 
PG-13
1436 
PG
683 
G
 
109
Not Rated
 
102
Other values (10)
 
160

Length

Max length9
Median length1
Mean length2.7028451
Min length1

Characters and Unicode

Total characters12825
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPG-13
2nd rowPG-13
3rd rowPG-13
4th rowPG-13
5th rowPG-13

Common Values

ValueCountFrequency (%)
R 2255
47.5%
PG-13 1436
30.3%
PG 683
 
14.4%
G 109
 
2.3%
Not Rated 102
 
2.1%
Unrated 58
 
1.2%
Approved 55
 
1.2%
X 13
 
0.3%
Passed 9
 
0.2%
NC-17 7
 
0.1%
Other values (5) 18
 
0.4%

Length

2024-04-10T14:35:19.514203image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
r 2255
46.5%
pg-13 1436
29.6%
pg 683
 
14.1%
g 109
 
2.2%
not 102
 
2.1%
rated 102
 
2.1%
unrated 58
 
1.2%
approved 55
 
1.1%
x 13
 
0.3%
passed 9
 
0.2%
Other values (6) 25
 
0.5%

Most occurring characters

ValueCountFrequency (%)
R 2357
18.4%
G 2238
17.5%
P 2135
16.6%
- 1450
11.3%
1 1446
11.3%
3 1436
11.2%
t 262
 
2.0%
e 224
 
1.7%
d 224
 
1.7%
a 169
 
1.3%
Other values (17) 884
 
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12825
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 2357
18.4%
G 2238
17.5%
P 2135
16.6%
- 1450
11.3%
1 1446
11.3%
3 1436
11.2%
t 262
 
2.0%
e 224
 
1.7%
d 224
 
1.7%
a 169
 
1.3%
Other values (17) 884
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12825
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 2357
18.4%
G 2238
17.5%
P 2135
16.6%
- 1450
11.3%
1 1446
11.3%
3 1436
11.2%
t 262
 
2.0%
e 224
 
1.7%
d 224
 
1.7%
a 169
 
1.3%
Other values (17) 884
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12825
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 2357
18.4%
G 2238
17.5%
P 2135
16.6%
- 1450
11.3%
1 1446
11.3%
3 1436
11.2%
t 262
 
2.0%
e 224
 
1.7%
d 224
 
1.7%
a 169
 
1.3%
Other values (17) 884
 
6.9%

budget
Real number (ℝ)

SKEWED 

Distinct432
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39308861
Minimum218
Maximum1.22155 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.2 KiB
2024-04-10T14:35:20.072333image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum218
5-th percentile800000
Q17500000
median20000000
Q340000000
95-th percentile1.25 × 108
Maximum1.22155 × 1010
Range1.22155 × 1010
Interquartile range (IQR)32500000

Descriptive statistics

Standard deviation2.0182661 × 108
Coefficient of variation (CV)5.1343796
Kurtosis2842.5051
Mean39308861
Median Absolute Deviation (MAD)15000000
Skewness49.200958
Sum1.8652054 × 1011
Variance4.0733982 × 1016
MonotonicityNot monotonic
2024-04-10T14:35:20.587853image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000000 448
 
9.4%
30000000 146
 
3.1%
15000000 143
 
3.0%
25000000 141
 
3.0%
10000000 137
 
2.9%
40000000 132
 
2.8%
35000000 121
 
2.6%
50000000 104
 
2.2%
5000000 103
 
2.2%
60000000 94
 
2.0%
Other values (422) 3176
66.9%
ValueCountFrequency (%)
218 1
 
< 0.1%
1100 1
 
< 0.1%
4500 1
 
< 0.1%
7000 3
0.1%
9000 1
 
< 0.1%
10000 2
< 0.1%
14000 1
 
< 0.1%
15000 2
< 0.1%
20000 3
0.1%
22000 1
 
< 0.1%
ValueCountFrequency (%)
1.22155 × 10101
< 0.1%
4200000000 1
< 0.1%
2500000000 1
< 0.1%
2400000000 1
< 0.1%
2127519898 1
< 0.1%
1100000000 1
< 0.1%
1000000000 1
< 0.1%
700000000 2
< 0.1%
600000000 1
< 0.1%
553632000 1
< 0.1%

title_year
Real number (ℝ)

Distinct91
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2002.1144
Minimum1916
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.2 KiB
2024-04-10T14:35:21.189850image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1916
5-th percentile1978
Q11999
median2005
Q32010
95-th percentile2015
Maximum2016
Range100
Interquartile range (IQR)11

Descriptive statistics

Standard deviation12.502688
Coefficient of variation (CV)0.0062447421
Kurtosis7.3303826
Mean2002.1144
Median Absolute Deviation (MAD)6
Skewness-2.2773413
Sum9500033
Variance156.31721
MonotonicityNot monotonic
2024-04-10T14:35:21.652371image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2009 254
 
5.4%
2006 236
 
5.0%
2010 222
 
4.7%
2008 222
 
4.7%
2014 218
 
4.6%
2011 215
 
4.5%
2005 215
 
4.5%
2013 214
 
4.5%
2004 209
 
4.4%
2012 206
 
4.3%
Other values (81) 2534
53.4%
ValueCountFrequency (%)
1916 1
< 0.1%
1920 1
< 0.1%
1925 1
< 0.1%
1927 1
< 0.1%
1929 2
< 0.1%
1930 1
< 0.1%
1932 1
< 0.1%
1933 2
< 0.1%
1934 1
< 0.1%
1935 1
< 0.1%
ValueCountFrequency (%)
2016 85
 
1.8%
2015 186
3.9%
2014 218
4.6%
2013 214
4.5%
2012 206
4.3%
2011 215
4.5%
2010 222
4.7%
2009 254
5.4%
2008 222
4.7%
2007 199
4.2%

actor_2_fb_likes
Real number (ℝ)

Distinct906
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1727.8936
Minimum0
Maximum137000
Zeros32
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size203.2 KiB
2024-04-10T14:35:23.005430image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33
Q1300
median617
Q3931
95-th percentile11000
Maximum137000
Range137000
Interquartile range (IQR)631

Descriptive statistics

Standard deviation4148.4537
Coefficient of variation (CV)2.4008734
Kurtosis244.61784
Mean1727.8936
Median Absolute Deviation (MAD)316
Skewness9.6427963
Sum8198855
Variance17209668
MonotonicityNot monotonic
2024-04-10T14:35:23.780429image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 302
 
6.4%
11000 111
 
2.3%
2000 98
 
2.1%
3000 75
 
1.6%
10000 47
 
1.0%
14000 41
 
0.9%
13000 40
 
0.8%
826 37
 
0.8%
4000 33
 
0.7%
0 32
 
0.7%
Other values (896) 3929
82.8%
ValueCountFrequency (%)
0 32
0.7%
2 11
 
0.2%
3 9
 
0.2%
4 11
 
0.2%
5 8
 
0.2%
6 7
 
0.1%
7 2
 
< 0.1%
8 9
 
0.2%
9 12
 
0.3%
10 8
 
0.2%
ValueCountFrequency (%)
137000 1
 
< 0.1%
29000 1
 
< 0.1%
27000 2
 
< 0.1%
25000 3
 
0.1%
23000 6
0.1%
22000 11
0.2%
21000 4
 
0.1%
20000 6
0.1%
19000 7
0.1%
18000 9
0.2%

imdb_score
Real number (ℝ)

Distinct76
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4322234
Minimum1.6
Maximum9.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.2 KiB
2024-04-10T14:35:25.147158image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.6
5-th percentile4.4
Q15.8
median6.6
Q37.2
95-th percentile8
Maximum9.3
Range7.7
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.1002781
Coefficient of variation (CV)0.1710572
Kurtosis1.0444812
Mean6.4322234
Median Absolute Deviation (MAD)0.7
Skewness-0.76831284
Sum30520.9
Variance1.2106119
MonotonicityNot monotonic
2024-04-10T14:35:27.092358image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.7 214
 
4.5%
6.6 192
 
4.0%
7.2 183
 
3.9%
6.5 183
 
3.9%
6.4 180
 
3.8%
6.8 178
 
3.8%
7 176
 
3.7%
7.3 175
 
3.7%
6.1 174
 
3.7%
7.1 173
 
3.6%
Other values (66) 2917
61.5%
ValueCountFrequency (%)
1.6 1
 
< 0.1%
1.7 1
 
< 0.1%
1.9 3
0.1%
2 2
< 0.1%
2.1 3
0.1%
2.2 3
0.1%
2.3 3
0.1%
2.4 2
< 0.1%
2.5 2
< 0.1%
2.6 1
 
< 0.1%
ValueCountFrequency (%)
9.3 1
 
< 0.1%
9.2 1
 
< 0.1%
9 2
 
< 0.1%
8.9 5
 
0.1%
8.8 5
 
0.1%
8.7 8
 
0.2%
8.6 11
 
0.2%
8.5 21
0.4%
8.4 23
0.5%
8.3 35
0.7%

aspect_ratio
Real number (ℝ)

Distinct20
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1256797
Minimum1.18
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.2 KiB
2024-04-10T14:35:27.985358image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.18
5-th percentile1.78
Q11.85
median2.35
Q32.35
95-th percentile2.35
Maximum16
Range14.82
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.63597663
Coefficient of variation (CV)0.29918743
Kurtosis379.54111
Mean2.1256797
Median Absolute Deviation (MAD)0
Skewness17.448477
Sum10086.35
Variance0.40446628
MonotonicityNot monotonic
2024-04-10T14:35:28.785362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2.35 2523
53.2%
1.85 1886
39.7%
1.37 97
 
2.0%
1.78 80
 
1.7%
1.66 63
 
1.3%
1.33 37
 
0.8%
2.2 15
 
0.3%
2.39 14
 
0.3%
16 8
 
0.2%
2 4
 
0.1%
Other values (10) 18
 
0.4%
ValueCountFrequency (%)
1.18 1
 
< 0.1%
1.2 1
 
< 0.1%
1.33 37
 
0.8%
1.37 97
2.0%
1.44 1
 
< 0.1%
1.5 2
 
< 0.1%
1.66 63
1.3%
1.75 3
 
0.1%
1.77 1
 
< 0.1%
1.78 80
1.7%
ValueCountFrequency (%)
16 8
 
0.2%
2.76 3
 
0.1%
2.55 2
 
< 0.1%
2.4 3
 
0.1%
2.39 14
 
0.3%
2.35 2523
53.2%
2.24 1
 
< 0.1%
2.2 15
 
0.3%
2 4
 
0.1%
1.85 1886
39.7%

movie_fb_likes
Real number (ℝ)

ZEROS 

Distinct836
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7822.658
Minimum0
Maximum349000
Zeros2104
Zeros (%)44.3%
Negative0
Negative (%)0.0%
Memory size203.2 KiB
2024-04-10T14:35:29.684160image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median181
Q35000
95-th percentile41800
Maximum349000
Range349000
Interquartile range (IQR)5000

Descriptive statistics

Standard deviation19644.251
Coefficient of variation (CV)2.511199
Kurtosis39.865391
Mean7822.658
Median Absolute Deviation (MAD)181
Skewness4.9492684
Sum37118512
Variance3.8589659 × 108
MonotonicityNot monotonic
2024-04-10T14:35:30.431363image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2104
44.3%
1000 103
 
2.2%
11000 81
 
1.7%
10000 79
 
1.7%
12000 60
 
1.3%
13000 58
 
1.2%
2000 54
 
1.1%
15000 52
 
1.1%
14000 47
 
1.0%
16000 46
 
1.0%
Other values (826) 2061
43.4%
ValueCountFrequency (%)
0 2104
44.3%
4 2
 
< 0.1%
5 1
 
< 0.1%
7 2
 
< 0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
12 2
 
< 0.1%
14 1
 
< 0.1%
16 1
 
< 0.1%
17 3
 
0.1%
ValueCountFrequency (%)
349000 1
< 0.1%
199000 1
< 0.1%
197000 1
< 0.1%
191000 1
< 0.1%
190000 1
< 0.1%
175000 1
< 0.1%
165000 1
< 0.1%
164000 1
< 0.1%
153000 1
< 0.1%
150000 1
< 0.1%

Interactions

2024-04-10T14:34:42.469921image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:32:54.407502image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:00.053482image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:05.759444image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:10.537152image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:16.573612image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:21.715797image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:27.072367image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:32.976340image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:43.911688image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:53.139193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:01.016644image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:11.011136image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:21.098053image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:28.351378image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:35.432402image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:42.734955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:32:54.723746image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:00.400144image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:06.217438image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:10.843306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:16.902679image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:22.053142image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:27.410067image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:34.467457image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:44.258690image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:53.480955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:01.505671image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:11.589133image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:21.483067image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:28.657380image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:36.107403image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:43.053975image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:32:55.063982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:00.697153image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:06.505480image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:12.661459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:17.202818image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:22.380109image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:27.721081image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:35.808828image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:44.526690image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:53.777951image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:01.951642image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:12.270143image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:21.771056image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:28.920382image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:37.145404image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:43.378973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:32:55.522962image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:00.988147image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:06.724106image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:12.917473image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:17.495864image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:22.662117image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:27.977066image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:37.173042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:44.808689image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:54.165953image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:02.390656image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:12.929135image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:22.405056image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:29.378381image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:38.088151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:43.674978image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:32:55.873483image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:01.567171image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:07.025144image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:13.196458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:17.782525image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:22.932106image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:28.261079image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:37.521584image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:45.727794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:54.487952image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:02.874639image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:14.143796image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:23.023056image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:29.746387image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:38.621156image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:44.517074image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:32:56.197477image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:02.019148image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:07.349145image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:13.478456image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:18.075521image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:23.544449image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:28.632769image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:37.938226image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:46.167848image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:55.222294image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:03.749643image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:15.034806image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:23.532055image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:30.083419image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:39.073146image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:44.825070image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:32:56.545528image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:02.518836image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:07.672110image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:13.762114image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:18.365559image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:23.865448image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:28.976378image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:38.381478image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:47.042480image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:55.604287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:04.523639image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:16.440317image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:23.946052image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:30.406383image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:39.394768image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:45.124281image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:32:57.003487image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:02.900476image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:07.971109image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:14.070149image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:18.724533image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:24.197443image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:29.322372image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:38.740482image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:48.318441image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:55.951526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:04.940639image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:16.943319image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:24.373097image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:30.750742image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:39.703773image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:45.451283image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:32:57.377480image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:03.265436image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:08.249104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:14.359138image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:19.197172image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:24.504462image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:29.641410image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:39.064170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:48.617479image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:56.262530image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:05.326640image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:17.410318image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:24.827139image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:31.482287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:39.994909image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:45.766813image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:32:57.712479image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:03.565436image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:08.499104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:14.621115image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:19.508208image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:24.803506image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:29.929374image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:39.357166image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:49.954681image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:56.780547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:05.749239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:17.793320image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:25.209170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:32.443297image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:40.260912image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:46.076083image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:32:58.060481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:03.866435image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:08.780109image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:14.886111image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:19.816211image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:25.113498image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:30.226233image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:39.605767image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:51.022943image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:57.710726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:06.054238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:18.100317image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:25.561997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:33.202297image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:40.611923image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:46.404574image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:32:58.405483image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:04.124464image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:09.031106image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:15.174799image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:20.117200image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:25.427461image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:30.501239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:40.127731image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:51.635978image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:58.194729image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:07.276868image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:18.569320image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:26.276000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:33.800290image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:40.969600image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:46.702563image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:32:58.695520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:04.392438image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:09.258169image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:15.444838image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:20.406173image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:25.686509image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:30.829234image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:41.287211image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:51.932606image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:58.675375image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:08.628938image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:18.949319image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:26.692000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:34.196286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:41.277913image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:47.023568image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:32:58.986521image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:04.760440image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:09.594112image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:15.741798image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:20.665757image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:26.069362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:31.158990image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:42.061212image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:52.212609image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:59.234377image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:09.564555image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:19.711688image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:27.047017image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:34.530288image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:41.603914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:47.326563image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:32:59.293483image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:05.100439image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:09.936106image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:15.985611image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:20.971766image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:26.427363image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:31.525737image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:42.464215image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:52.481644image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:59.717603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:10.006132image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:20.158634image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:27.520994image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:34.849399image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:41.893883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:47.605572image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:32:59.677480image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:05.423447image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:10.244127image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:16.285609image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:21.363758image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:26.745362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:32.384740image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:43.533043image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:33:52.795193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:00.287116image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:10.406133image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:20.517953image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:27.852381image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:35.154437image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-10T14:34:42.184983image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-04-10T14:34:48.097151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-10T14:34:49.786857image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

director_namenum_critic_for_reviewsdurationdirector_fb_likesactor_3_fb_likesactor_2_nameactor_1_fb_likesgrossgenresactor_1_namemovie_titlenum_voted_userscast_total_fb_likesactor_3_namefacenumber_in_posterplot_keywordsnum_user_for_reviewscountrycontent_ratingbudgettitle_yearactor_2_fb_likesimdb_scoreaspect_ratiomovie_fb_likes
0James Cameron723.0178.00.0855.0Joel David Moore1000.0760505847.0Action|Adventure|Fantasy|Sci-FiCCH PounderAvatar8862044834Wes Studi0.0avatar|future|marine|native|paraplegic3054.0USAPG-13237000000.02009.0936.07.91.7833000
1Gore Verbinski302.0169.0563.01000.0Orlando Bloom40000.0309404152.0Action|Adventure|FantasyJohnny DeppPirates of the Caribbean: At World's End47122048350Jack Davenport0.0goddess|marriage ceremony|marriage proposal|pirate|singapore1238.0USAPG-13300000000.02007.05000.07.12.350
2Sam Mendes602.0148.00.0161.0Rory Kinnear11000.0200074175.0Action|Adventure|ThrillerChristoph WaltzSpectre27586811700Stephanie Sigman1.0bomb|espionage|sequel|spy|terrorist994.0UKPG-13245000000.02015.0393.06.82.3585000
3Christopher Nolan813.0164.022000.023000.0Christian Bale27000.0448130642.0Action|ThrillerTom HardyThe Dark Knight Rises1144337106759Joseph Gordon-Levitt0.0deception|imprisonment|lawlessness|police officer|terrorist plot2701.0USAPG-13250000000.02012.023000.08.52.35164000
5Andrew Stanton462.0132.0475.0530.0Samantha Morton640.073058679.0Action|Adventure|Sci-FiDaryl SabaraJohn Carter2122041873Polly Walker1.0alien|american civil war|male nipple|mars|princess738.0USAPG-13263700000.02012.0632.06.62.3524000
6Sam Raimi392.0156.00.04000.0James Franco24000.0336530303.0Action|Adventure|RomanceJ.K. SimmonsSpider-Man 338305646055Kirsten Dunst0.0sandman|spider man|symbiote|venom|villain1902.0USAPG-13258000000.02007.011000.06.22.350
7Nathan Greno324.0100.015.0284.0Donna Murphy799.0200807262.0Adventure|Animation|Comedy|Family|Fantasy|Musical|RomanceBrad GarrettTangled2948102036M.C. Gainey1.017th century|based on fairy tale|disney|flower|tower387.0USAPG260000000.02010.0553.07.81.8529000
8Joss Whedon635.0141.00.019000.0Robert Downey Jr.26000.0458991599.0Action|Adventure|Sci-FiChris HemsworthAvengers: Age of Ultron46266992000Scarlett Johansson4.0artificial intelligence|based on comic book|captain america|marvel cinematic universe|superhero1117.0USAPG-13250000000.02015.021000.07.52.35118000
9David Yates375.0153.0282.010000.0Daniel Radcliffe25000.0301956980.0Adventure|Family|Fantasy|MysteryAlan RickmanHarry Potter and the Half-Blood Prince32179558753Rupert Grint3.0blood|book|love|potion|professor973.0UKPG250000000.02009.011000.07.52.3510000
10Zack Snyder673.0183.00.02000.0Lauren Cohan15000.0330249062.0Action|Adventure|Sci-FiHenry CavillBatman v Superman: Dawn of Justice37163924450Alan D. Purwin0.0based on comic book|batman|sequel to a reboot|superhero|superman3018.0USAPG-13250000000.02016.04000.06.92.35197000
director_namenum_critic_for_reviewsdurationdirector_fb_likesactor_3_fb_likesactor_2_nameactor_1_fb_likesgrossgenresactor_1_namemovie_titlenum_voted_userscast_total_fb_likesactor_3_namefacenumber_in_posterplot_keywordsnum_user_for_reviewscountrycontent_ratingbudgettitle_yearactor_2_fb_likesimdb_scoreaspect_ratiomovie_fb_likes
5026Olivier Assayas81.0110.0107.045.0Béatrice Dalle576.0136007.0Drama|Music|RomanceMaggie CheungClean3924776Don McKellar1.0jail|junkie|money|motel|singer39.0OtherR4500.02004.0133.06.92.35171
5027Jafar Panahi64.090.0397.00.0Nargess Mamizadeh5.0673780.0DramaFereshteh Sadre OrafaiyThe Circle45555Mojgan Faramarzi0.0abortion|bus|hospital|prison|prostitution26.0OtherNot Rated10000.02000.00.07.51.85697
5029Kiyoshi Kurosawa78.0111.062.06.0Anna Nakagawa89.094596.0Crime|Horror|Mystery|ThrillerKôji YakushoThe Cure6318115Denden0.0breasts|interrogation|investigation|murder|watching television50.0OtherR1000000.01997.013.07.41.85817
5032Ash Baron-Cohen10.098.03.0152.0Stanley B. Herman789.024848292.0Crime|DramaPeter GreeneBang4381186James Noble1.0corruption|homeless|homeless man|motorcycle|urban legend14.0USAR20000000.01995.0194.06.42.3520
5033Shane Carruth143.077.0291.08.0David Sullivan291.0424760.0Drama|Sci-Fi|ThrillerShane CarruthPrimer72639368Casey Gooden0.0changing the future|independent film|invention|nonlinear timeline|time travel371.0USAPG-137000.02004.045.07.01.8519000
5034Neill Dela Llana35.080.00.00.0Edgar Tancangco0.070071.0ThrillerIan GamazonCavite5890Quynn Ton0.0jihad|mindanao|philippines|security guard|squatter35.0OtherNot Rated7000.02005.00.06.32.3574
5035Robert Rodriguez56.081.00.06.0Peter Marquardt121.02040920.0Action|Crime|Drama|Romance|ThrillerCarlos GallardoEl Mariachi52055147Consuelo Gómez0.0assassin|death|guitar|gun|mariachi130.0USAR7000.01992.020.06.91.370
5037Edward Burns14.095.00.0133.0Caitlin FitzGerald296.04584.0Comedy|DramaKerry BishéNewlyweds1338690Daniella Pineda1.0written and directed by cast member14.0USANot Rated9000.02011.0205.06.42.35413
5038Scott Smith1.087.02.0318.0Daphne Zuniga637.024848292.0Comedy|DramaEric MabiusSigned Sealed Delivered6292283Crystal Lowe2.0fraud|postal worker|prison|theft|trial6.0OtherR20000000.02013.0470.07.72.3584
5042Jon Gunn43.090.016.016.0Brian Herzlinger86.085222.0DocumentaryJohn AugustMy Date with Drew4285163Jon Gunn0.0actress name in title|crush|date|four word title|video camera84.0USAPG1100.02004.023.06.61.85456

Duplicate rows

Most frequently occurring

director_namenum_critic_for_reviewsdurationdirector_fb_likesactor_3_fb_likesactor_2_nameactor_1_fb_likesgrossgenresactor_1_namemovie_titlenum_voted_userscast_total_fb_likesactor_3_namefacenumber_in_posterplot_keywordsnum_user_for_reviewscountrycontent_ratingbudgettitle_yearactor_2_fb_likesimdb_scoreaspect_ratiomovie_fb_likes# duplicates
0Albert Hughes208.0122.0117.0140.0Jason Flemyng40000.031598308.0Horror|Mystery|ThrillerJohnny DeppFrom Hell12476541636Ian Richardson1.0freemason|jack the ripper|opium|prostitute|victorian era541.0USAR35000000.02001.01000.06.82.3502
1Angelina Jolie Pitt322.0137.011000.0465.0Jack O'Connell769.0115603980.0Biography|Drama|Sport|WarFinn WittrockUnbroken1035892938Alex Russell0.0emaciation|male nudity|plane crash|prisoner of war|torture351.0USAPG-1365000000.02014.0698.07.22.35350002
2Bill Condon322.0115.0386.012000.0Kristen Stewart21000.0292298923.0Adventure|Drama|Fantasy|RomanceRobert PattinsonThe Twilight Saga: Breaking Dawn - Part 218539459177Taylor Lautner3.0battle|friend|super strength|vampire|vision329.0USAPG-13120000000.02012.017000.05.52.35650002
3Brett Ratner245.0101.0420.0467.0Rufus Sewell12000.072660029.0Action|AdventureDwayne JohnsonHercules11568716235Ingrid Bolsø Berdal0.0army|greek mythology|hercules|king|mercenary269.0USAPG-13100000000.02014.03000.06.02.35210002
4Bruce McCulloch52.085.054.0455.0Megan Mullally985.013973532.0Comedy|CrimeMartin StarrStealing Harvard112113065Chris Penn1.0black humor|crying during sex|harvard|humor|man with glasses92.0USAPG-1325000000.02002.0637.05.11.852152
5Danny Boyle393.0101.00.0888.0Spencer Wilding3000.02319187.0Crime|Drama|Mystery|ThrillerRosario DawsonTrance926405056Tuppence Middleton0.0amnesia|criminal|heist|hypnotherapy|lost painting212.0UKR20000000.02013.01000.07.02.35230002
6David Hewlett8.088.0686.0405.0David Hewlett847.024848292.0ComedyChristopher JudgeA Dog's Breakfast32622364Paul McGillion2.0dog|vegetarian46.0OtherR120000.02007.0686.07.01.783772
7David Yates248.0110.0282.0103.0Alexander Skarsgård11000.0124051759.0Action|Adventure|Drama|RomanceChristoph WaltzThe Legend of Tarzan4237221175Casper Crump2.0africa|capture|jungle|male objectification|tarzan239.0USAPG-13180000000.02016.010000.06.62.35290002
8Frank Oz168.087.00.0548.0Ewen Bremner22000.08579684.0ComedyPeter DinklageDeath at a Funeral8954724324Kris Marshall0.0end credits roll call|four word title|funeral|secret|uncle199.0USAR9000000.02007.0557.07.41.8502
9George A. Romero284.096.00.056.0Duane Jones125.0236452.0Drama|Horror|MysteryJudith O'DeaNight of the Living Dead87978403S. William Hinzman5.0cemetery|farmhouse|radiation|running out of gas|zombie580.0USAUnrated114000.01968.0108.08.01.8502